🐍 MEDUSA - AI Security Scanner

AI-first security scanner with 40,000+ detection patterns for AI/ML, agents, and LLM applications. 🤖 Works out of the box - no tool installation required. 🚨 200 CVEs: Log4Shell, Spring4Shell, XZ Utils, LangChain RCE, MCP-Remote RCE, React2Shell 🔥 medusa scan --git <URL> — Scan any repo for AI supply chain attacks (repo poisoning, prompt injection, MCP tool poisoning) 🔐 medusa secrets scan — Find leaked API keys in your Claude / Cursor / Copilot / shell history. 21 issuer types. Interactive in-place redaction. 🚀 v2026.5.12: Our biggest release ever — 40,000+ detection patterns (up from 9,600+), harvested from 8,466 AI-security research papers and false-positive-hardened against real-world codebases.

🎯 What is MEDUSA?

MEDUSA is an AI-first security scanner with 40,000+ detection patterns that works out of the box. Simply install and scan - no external tool installation required. MEDUSA's built-in rules detect vulnerabilities in AI/ML applications, LLM agents, MCP servers, RAG pipelines, and traditional code.

✨ Key Features

• 🔥 medusa scan --git <URL> - Scan any GitHub repo for AI supply chain attacks in seconds
• 🔐 medusa secrets scan + purge - Find API keys / tokens / private keys leaked into Claude Code / Cursor / Copilot / Zed / Gemini chat histories and your bash / zsh / psql / mysql / python REPL history. 21 issuer types (Anthropic, OpenAI, PyPI, GitHub PATs, AWS, GCP, Stripe, Slack…). Interactive [y/n/s/a/q] purge with mandatory byte-identical backup and JSONL-safe redaction. Local-only, no telemetry.
• 🤖 40,000+ AI Security Patterns - Industry-leading coverage for AI/ML, agents, and LLM applications
• 🛡️ Repo Poisoning Detection - Detects weaponized AI editor configs across 28+ file types (Cursor, Cline, Copilot, Claude Code, Gemini, Kiro, and more)
• 🚀 Zero Setup Required - Works immediately after pip install - no tool installation needed
• 🚨 200 CVE Detections - Log4Shell, Spring4Shell, XZ Utils backdoor, LangChain RCE, MCP remote code execution, React2Shell, and more
• ⚡ Parallel Processing - Multi-core scanning (10-40x faster than sequential), works on macOS/Windows/Linux
• 🎨 Beautiful CLI - Rich terminal output with progress bars
• 🧠 IDE Integration - Claude Code, Cursor, VS Code, Gemini CLI support
• 🔄 Smart Caching - Skip unchanged files for lightning-fast rescans (content-hash keyed, correct in CI)
• ⚙️ Configurable - .medusa.yml for project-specific settings
• 🌍 Cross-Platform - Native Windows, macOS, and Linux support
• 📊 Multiple Reports - JSON, HTML, Markdown, SARIF exports for any workflow
• 🔧 Optional Linter Support - Auto-detects external linters if installed for enhanced coverage

🆕 What's New in v2026.5.12

Our biggest release ever — 4× the detection coverage, false-positive-hardened.

External Linters (optional): MEDUSA auto-detects bandit, eslint, shellcheck, etc. if installed. See Optional Tools Guide.

🔐 Scan your AI chat history for leaked secrets

Your PyPI token might be in your Claude chat history right now.

Developers paste API keys, tokens, and credentials into AI assistants every day — "deploy this with pypi-AgEI...", "use my ghp_... to push", "the AWS key is AKIA...". The assistants keep those conversations in plaintext on disk. Anyone with read access to $HOME — or any future malware with shell access — can grep -r 'sk-\|ghp_\|AKIA' ~/ and harvest production credentials in seconds.

medusa secrets scan finds them. medusa secrets purge cleans them up.

30-second tour

medusa secrets scan

Scanning 118 file(s)...

── claude-code ──────────────────────────────────────────────
/home/ross/.claude/history.jsonl  (13 finding(s))
[CRITICAL] Anthropic API key (anthropic)
    /home/ross/.claude/history.jsonl:1005:13
    sk-ant-api03***...***
[CRITICAL] PyPI API token (pypi)
    /home/ross/.claude/history.jsonl:125:94
    pypi-AgEIc***...***
[CRITICAL] GitHub fine-grained PAT (github)
    /home/ross/.claude/history.jsonl:2306:13
    github_pat_11A***...***
[HIGH]     HuggingFace token (huggingface)
    /home/ross/.claude/history.jsonl:3387:13
    hf_JOi***...***
...

Total: 13 credentials across 1 file(s).
Report:  /home/ross/.medusa/secrets-scan/secrets-20260519-074452.json

medusa secrets purge

[CRITICAL] PyPI API token  (pypi)
    /home/ross/.claude/history.jsonl:125:94
    pypi-AgEIc***...***
  redact?  [y/n/s/a/q/?]: y

[CRITICAL] Anthropic API key  (anthropic)
    /home/ross/.claude/history.jsonl:1005:13
    sk-ant-api03***...***
  redact?  [y/n/s/a/q/?]: y
...

✓ /home/ross/.claude/history.jsonl  (13 redacted)
    backup → /home/ross/.medusa/secrets-scan/backups/20260519-074452/home/ross/.claude/history.jsonl

The original file is backed up byte-for-byte before any change. JSONL stays parseable after redaction. The redaction marker ([REDACTED-MEDUSA-...-<run-id>]) is unique per run so you can trace it back to the scan that produced it.

Commands

medusa secrets scan                       # everything (default — chat + shell)
medusa secrets scan --source ai-chats     # AI assistants only
medusa secrets scan --source shell        # ~/.bash_history, ~/.zsh_history, fish, psql, mysql, ...
medusa secrets scan --path FILE           # explicit file (e.g. a ChatGPT export)
medusa secrets scan --reveal              # show real values (requires 'I UNDERSTAND')

medusa secrets purge                      # interactive [y/n/s/a/q]
medusa secrets purge SCAN_ID              # purge a specific report
medusa secrets purge --all --yes-i-know   # batch mode for power users / CI

What's detected (21 issuers)

AI providers: Anthropic, OpenAI, HuggingFace, Replicate, Cohere Package registries: PyPI, npm Source forges: GitHub PAT (classic + fine-grained + OAuth + App), GitLab PAT Cloud: AWS access keys, GCP service-account JSON Payments / comms: Stripe live/restricted keys, Slack bot/user tokens, SendGrid, Twilio, Discord webhooks Cryptography: PEM-encoded private keys (RSA, DSA, EC, OpenSSH, PGP)

Safety properties

• Local-only. Reports under ~/.medusa/secrets-scan/ mode 0o600. No network, no telemetry, never written to project trees.
• Backup before write. Every redaction is preceded by a byte-identical backup. cp restores it.
• JSONL-safe. The redaction marker contains no JSON-unsafe characters; affected lines are re-parsed after the write.
• Refuse on drift. If the source file changed between scan and purge, the purger refuses rather than risk clobbering an edit.
• Atomic write. Temp file + os.replace swap. Either the rewrite lands or the original stays.

📖 Full secrets-scanner guide →

🚀 Quick Start

Installation

# Install MEDUSA (works on Windows, macOS, Linux)
pip install medusa-security

# Run your first scan - that's it!
medusa scan .

Virtual Environment (Recommended):

# Create and activate virtual environment
python3 -m venv medusa-env
source medusa-env/bin/activate  # On Windows: medusa-env\Scripts\activate

# Install and scan
pip install medusa-security
medusa scan .

Platform Notes:

• Windows: Use py -m medusa if medusa command is not found
• macOS/Linux: Should work out of the box

Scan Any GitHub Repo

# Scan a remote repo for AI supply chain attacks
medusa scan --git https://github.com/org/repo

# Shorthand - just user/repo
medusa scan --git org/repo

# Scan a specific branch
medusa scan --git https://github.com/org/repo/tree/main

MEDUSA automatically detects 28+ AI editor config files that are known attack vectors:

Known attacks detected: Clinejection, CurXecute (CVE-2025-54135), IDEsaster (CVE-2025-64660), ToxicSkills, CamoLeak, RoguePilot, AIShellJack, Cacheract

Optional: AI Model Scanning

# Install modelscan for ML model vulnerability detection
medusa install --ai-tools

Optional: External Linters

MEDUSA auto-detects external linters if installed (bandit, eslint, shellcheck, etc.) and uses them automatically to enhance scan coverage.

See Installation Guide → for platform-specific instructions.

Note: External linters are optional. MEDUSA's 40,000+ built-in rules work without them. For installation support, please refer to each tool vendor's documentation.

Demo

📊 Report Formats

MEDUSA generates beautiful reports in multiple formats:

JSON - Machine-readable for CI/CD integration

medusa scan . --format json

HTML - Stunning glassmorphism UI with interactive charts

medusa scan . --format html

Markdown - Documentation-friendly for GitHub/wikis

medusa scan . --format markdown

All Formats - Generate everything at once

medusa scan . --format all

📚 Language Support

MEDUSA supports 79 scanner types covering AI/ML security, all major programming languages, and file formats:

Backend Languages (9)

JVM Languages (3)

Functional Languages (5)

Mobile Development (2)

Frontend & Styling (3)

Infrastructure as Code (4)

Configuration Files (4)

Shell & Scripts (4)

Documentation (2)

Other Languages (5)

Total: 79 scanner types — 41 language/tool scanners + 38 AI/ML security scanners — covering 100+ file extensions

🚨 React2Shell CVE Detection (NEW in v2025.8)

MEDUSA now detects CVE-2025-55182 "React2Shell" - a CVSS 10.0 RCE vulnerability affecting React Server Components and Next.js.

# Check if your project is vulnerable
medusa scan .

# Vulnerable versions detected:
# - React 19.0.0 - 19.2.0 (Server Components)
# - Next.js 15.0.0 - 15.0.4 (App Router)
# - Various canary/rc releases

Scans: package.json, package-lock.json, yarn.lock, pnpm-lock.yaml

Fix: Upgrade to React 19.0.1+ and Next.js 15.0.5+

🤖 AI Agent Security

MEDUSA provides industry-leading AI security scanning with 40,000+ detection patterns for the agentic AI era. Updated for OWASP Top 10 for LLM Applications 2025 and includes detection for 200+ CVEs across AI coding editors and MCP servers.

Full AI Security Documentation

AI Security Coverage

AI Attack Coverage

Supported AI Files (28+)

# Critical - Known RCE vectors
.cursorrules              # Cursor AI (CVE-2025-54135)
.cursor/rules/*.mdc       # Cursor rules directory
.cursor/mcp.json          # Cursor MCP (CurXecute RCE)
.clinerules/*.md          # Cline (Clinejection)
.windsurfrules            # Windsurf (CVE-2025-36730)
.windsurf/rules/*         # Windsurf workspace rules
.codex/config.toml        # Codex CLI (CVE-2025-61260)
.kiro/settings/mcp.json   # Kiro (CVE-2026-0830)
.vscode/settings.json     # VS Code (IDEsaster)
*.code-workspace          # VS Code workspace
mcp.json / .mcp.json      # MCP server configs

# High - AI instruction files
CLAUDE.md                 # Claude Code
GEMINI.md                 # Gemini CLI
AGENTS.md                 # OpenAI Codex
AGENT.md                  # Roo Code
SKILL.md                  # ClawHub/ToxicSkills
CONVENTIONS.md            # Aider
.github/copilot-instructions.md  # GitHub Copilot
.amazonq/rules/*.md       # Amazon Q Developer
.augment/rules/*          # Augment Code
.roo/rules/*.md           # Roo Code
.tabnine/guidelines/*.md  # Tabnine
.continue/config.yaml     # Continue.dev
.cody.yml                 # Sourcegraph Cody

Quick AI Security Scan

# Scan AI configuration files
medusa scan . --ai-only

# Example output:
# 🔍 AI Security Scan Results
# ├── .cursorrules: 3 issues (1 CRITICAL, 2 HIGH)
# │   └── AIC001: Prompt injection - ignore previous instructions (line 15)
# │   └── AIC011: Tool shadowing - override default tools (line 23)
# ├── mcp-config.json: 2 issues (2 HIGH)
# │   └── MCP003: Dangerous path - home directory access (line 8)
# └── rag_config.json: 1 issue (1 CRITICAL)
#     └── AIR010: Knowledge base injection pattern detected (line 45)

🎮 Usage

Basic Commands

# Initialize configuration
medusa init

# Scan current directory
medusa scan .

# Scan specific directory
medusa scan /path/to/project

# Quick scan (changed files only)
medusa scan . --quick

# Force full scan (ignore cache)
medusa scan . --force

# Use specific number of workers
medusa scan . --workers 4

# Fail on HIGH severity or above
medusa scan . --fail-on high

# Custom output directory
medusa scan . -o /tmp/reports

Install Commands

# Check tool status
medusa install --check

# Install AI tools (modelscan for ML model scanning)
medusa install --ai-tools

# Show detailed output
medusa install --ai-tools --debug

Note: MEDUSA v2026.2+ no longer installs external linters. Install them via your package manager (apt, brew, npm, pip) if needed. MEDUSA auto-detects and uses any installed linters.

Init Commands

# Interactive initialization wizard
medusa init

# Initialize with specific IDE
medusa init --ide claude-code

# Initialize with multiple IDEs
medusa init --ide claude-code --ide gemini-cli --ide cursor

# Initialize with all supported IDEs
medusa init --ide all

# Force overwrite existing config
medusa init --force

# Initialize and install tools
medusa init --install

Additional Commands

# Uninstall modelscan
medusa uninstall modelscan

# Check for updates
medusa version --check-updates

# Show current configuration
medusa config

# Override scanner for specific file
medusa override path/to/file.yaml YAMLScanner

# List available scanners
medusa override --list

# Show current overrides
medusa override --show

# Remove override
medusa override path/to/file.yaml --remove

Scan Options Reference

Install Options Reference

v2026.2+ Change: MEDUSA no longer manages external linter installation. The --all flag is deprecated. Install external linters via your system package manager if needed.

⚙️ Configuration

.medusa.yml

MEDUSA uses a YAML configuration file for project-specific settings:

# MEDUSA Configuration File
version: 2026.5.5

# Scanner control
scanners:
  enabled: []      # Empty = all scanners enabled
  disabled: []     # List scanners to disable

# Build failure settings
fail_on: high      # critical | high | medium | low

# Exclusion patterns
exclude:
  paths:
    - node_modules/
    - venv/
    - .venv/
    - .git/
    - __pycache__/
    - dist/
    - build/
  files:
    - "*.min.js"
    - "*.min.css"

# IDE integration
ide:
  claude_code:
    enabled: true
    auto_scan: true
  cursor:
    enabled: false
  vscode:
    enabled: false

# Scan settings
workers: null        # null = auto-detect CPU cores
cache_enabled: true  # Enable file caching for speed

Generate Default Config

medusa init

This creates .medusa.yml with sensible defaults and auto-detects your IDE.

🤖 IDE Integration

MEDUSA supports 5 major AI coding assistants with native integrations. Initialize with medusa init --ide all or select specific platforms.

Supported Platforms

Quick Setup

# Setup for all IDEs (recommended)
medusa init --ide all

# Or select specific platforms
medusa init --ide claude-code --ide gemini-cli

Claude Code

What it creates:

• CLAUDE.md - Project context file
• .claude/agents/medusa/agent.json - Agent configuration
• .claude/commands/medusa-scan.md - Scan slash command
• .claude/commands/medusa-install.md - Install slash command

Usage:

Type: /medusa-scan
Claude: *runs security scan*
Results: Displayed in terminal + chat

Gemini CLI

What it creates:

• GEMINI.md - Project context file
• .gemini/commands/scan.toml - Scan command config
• .gemini/commands/install.toml - Install command config

Usage:

gemini /scan              # Full scan
gemini /scan --quick      # Quick scan
gemini /install --check   # Check tools

OpenAI Codex

What it creates:

• AGENTS.md - Project context (root level)

Usage:

Ask: "Run a security scan"
Codex: *executes medusa scan .*

GitHub Copilot

What it creates:

• .github/copilot-instructions.md - Security standards and best practices

How it helps:

• Knows project security standards
• Suggests secure code patterns
• Recommends running scans after changes
• Helps fix security issues

Cursor

What it creates:

• .cursor/mcp-config.json - MCP server configuration
• Reuses .claude/ structure (Cursor is VS Code fork)

Usage:

• Works like Claude Code integration
• MCP-native for future deeper integration

🔧 Advanced Features

System Load Monitoring

MEDUSA automatically monitors system load and adjusts worker count:

# Auto-detects optimal workers based on:
# - CPU usage
# - Memory usage
# - Load average
# - Available cores

# Warns when system is overloaded:
⚠️  High CPU usage: 85.3%
Using 2 workers (reduced due to system load)

Smart Caching

Hash-based caching skips unchanged files:

# First scan
📂 Files scanned: 145
⏱️  Total time: 47.28s

# Second scan (no changes)
📂 Files scanned: 0
⚡ Files cached: 145
⏱️  Total time: 2.15s  # 22× faster!

Parallel Processing

Multi-core scanning for massive speedups:

Single-threaded:  417.5 seconds
6 workers:         47.3 seconds  # 8.8× faster
24 workers:        ~18 seconds   # 23× faster

📊 Example Workflow

New Project Setup

# 1. Initialize
cd my-awesome-project
medusa init

🐍 MEDUSA Initialization Wizard

✅ Step 1: Project Analysis
   Found 15 language types
   Primary: PythonScanner (44 files)

✅ Step 2: Scanner Availability
   Available: 6/79 scanners
   Missing: 73 tools

✅ Step 3: Configuration
   Created .medusa.yml
   Auto-detected IDE: Claude Code

✅ Step 4: IDE Integration
   Created .claude/agents/medusa/agent.json
   Created .claude/commands/medusa-scan.md

✅ MEDUSA Initialized Successfully!

# 2. First scan
medusa scan .

🔍 Issues found: 23
   CRITICAL: 0
   HIGH: 2
   MEDIUM: 18
   LOW: 3

# 3. Fix issues and rescan
medusa scan . --quick

⚡ Files cached: 142
🔍 Issues found: 12  # Progress!

CI/CD Integration

# .github/workflows/security.yml
name: Security Scan

on: [push, pull_request]

jobs:
  medusa:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3

      - name: Set up Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.11'

      - name: Install MEDUSA
        run: pip install medusa-security

      - name: Run security scan
        run: medusa scan . --fail-on high

Note: No tool installation step needed - MEDUSA's 40,000+ built-in rules work immediately.

🏗️ Architecture

Scanner Pattern

All scanners follow a consistent pattern:

class PythonScanner(BaseScanner):
    """Scanner for Python files using Bandit"""

    def get_tool_name(self) -> str:
        return "bandit"

    def get_file_extensions(self) -> List[str]:
        return [".py"]

    def scan_file(self, file_path: Path) -> ScannerResult:
        # Run bandit on file
        # Parse JSON output
        # Map severity levels
        # Return structured issues
        return ScannerResult(...)

Auto-Registration

Scanners automatically register themselves:

# medusa/scanners/__init__.py
registry = ScannerRegistry()
registry.register(PythonScanner())
registry.register(JavaScriptScanner())
# ... all 79 scanners

Severity Mapping

Unified severity levels across all tools:

• CRITICAL - Security vulnerabilities, fatal errors
• HIGH - Errors, security warnings
• MEDIUM - Warnings, code quality issues
• LOW - Style issues, conventions
• INFO - Suggestions, refactoring opportunities

🧪 Testing & Quality

Dogfooding Results

MEDUSA scans itself — and real-world projects:

Self-scan (473 files):
  ✅ Issues found: 114 (pre-filter) → 0 (post-filter)
  ✅ FP reduction: 100% on own codebase
  ⏱️  Time: 8.2s

OpenClaw benchmark (4,124 files, 751K LOC):
  🔍 Issues found: 825 (post-filter)
  ✅ FPs filtered: 11,436 (93.9% reduction)
  ⏱️  Time: 3.3 hours (79 scanners)

Performance Benchmarks

*Large project time dominated by external tool subprocesses (Semgrep, Trivy, GitLeaks). Built-in pattern scanning is near-instant.

🗺️ Roadmap

✅ Completed (v2026.5.0)

• medusa scan --git <URL> - Scan any GitHub repo for AI supply chain attacks
• Repo Poisoning Detection - 45 new rules for Clinejection, CurXecute, IDEsaster, CamoLeak, ToxicSkills
• 28+ AI Editor Config Detection - Priority file scanning across 15+ AI coding tools
• MCP Advanced Attacks - Schema poisoning, ATPA, sampling injection, cross-server manipulation
• 40,000+ Detection Patterns - Industry-leading AI security coverage
• 78 Specialized Analyzers - Comprehensive language and platform coverage
• 133 Critical CVEs - CVEMiner database for known vulnerability scanning
• 583 FP Filter Patterns - 97.7% false positive reduction rate on real-world projects
• Agent Protocol Security - UCP, AP2, ACP vulnerability detection (91 rules)
• Dataset Poisoning Detection - CSV, JSON, JSONL injection scanning
• Code-Level Prompt Injection - F-string injection, ChatML tokens, role manipulation
• Cross-Platform - Native Windows, macOS, Linux support
• IDE Integration - Claude Code, Cursor, Gemini CLI, GitHub Copilot, OpenAI Codex

🔮 Upcoming

• MEDUSA Professional - Runtime proxy filters for production LLM protection
• GitHub App - Automatic PR scanning
• VS Code Extension - Native IDE integration
• REST API - CI/CD pipeline integration

🤝 Contributing

We welcome contributions! Here's how to get started:

# 1. Fork and clone
git clone https://github.com/yourusername/medusa.git
cd medusa

# 2. Create virtual environment
python -m venv .venv
source .venv/bin/activate  # or `.venv\Scripts\activate` on Windows

# 3. Install in editable mode
pip install -e ".[dev]"

# 4. Run tests
pytest

# 5. Create feature branch
git checkout -b feature/my-awesome-feature

# 6. Make changes and test
medusa scan .  # Dogfood your changes!

# 7. Submit PR
git push origin feature/my-awesome-feature

Adding New Scanners

See docs/development/adding-scanners.md for a guide on adding new language support.

📜 License

AGPL-3.0-or-later - See LICENSE file

MEDUSA is free and open source software. You can use, modify, and distribute it freely, but any modifications or derivative works (including SaaS deployments) must also be released under AGPL-3.0.

For commercial licensing options, contact: support@pantheonsecurity.io

Coming Soon

MEDUSA Professional adds runtime protection for production LLM applications - blocking prompt injection, jailbreaking, and data exfiltration attempts in real-time before they reach your models.

The runtime proxy is currently in private beta. If you're protecting production LLM applications and want early access, reach out to support@pantheonsecurity.io.

🙏 Credits

Development:

• Pantheon Security
• Claude AI (Anthropic) - AI-assisted development

Built With:

• Python 3.10+
• Click - CLI framework
• Rich - Terminal formatting
• Bandit, ESLint, ShellCheck, and 39+ other open-source security tools

Inspired By:

• Bandit (Python security)
• SonarQube (multi-language analysis)
• Semgrep (pattern-based security)
• Mega-Linter (comprehensive linting)

📖 Guides

• Quick Start - Get running in 5 minutes
• AI Security Scanning - Complete guide to AI/LLM security (OWASP 2025, MCP, RAG)
• Handling False Positives - Reduce noise, find real issues
• IDE Integration - Setup Claude Code, Gemini, Copilot

📞 Support

• GitHub Issues: Report bugs or request features
• Email: support@pantheonsecurity.io
• Documentation: https://docs.pantheonsecurity.io
• Discord: https://discord.gg/medusa (coming soon)

📈 Statistics

Version: 2026.5.5 Release Date: 2026-04-03 Detection Patterns: 40,000+ AI security rules Analyzers: 79 specialized scanners FP Filter Patterns: 514 intelligent filters (96.8% reduction rate) CVE Coverage: 200 critical vulnerabilities (37+ AI editor CVEs) Repo Poisoning: 28+ AI editor config file types detected Language Coverage: 46+ file types Platform Support: Linux, macOS, Windows AI Integration: Claude Code, Gemini CLI, GitHub Copilot, Cursor, OpenAI Codex Standards: OWASP Top 10 for LLM 2025, MITRE ATLAS Downloads: 11,500+ on PyPI

🌟 Why MEDUSA?

vs. Bandit

• ✅ 40,000+ patterns (not just Python security)
• ✅ AI/ML security coverage
• ✅ Zero setup required
• ✅ IDE integration

vs. SonarQube

• ✅ Simpler setup (pip install && scan)
• ✅ No server required
• ✅ AI-first security focus
• ✅ Free and open source

vs. Semgrep

• ✅ AI/ML-specific rules built-in
• ✅ MCP, RAG, agent security
• ✅ Better IDE integration
• ✅ No rule configuration needed

vs. Traditional SAST

• ✅ Works immediately (no tool installation)
• ✅ AI security patterns included
• ✅ Parallel processing
• ✅ Smart caching

🐍🐍🐍 MEDUSA - Multi-Language Security Scanner 🐍🐍🐍

One Command. Complete Security.

medusa init && medusa scan .

Last Updated: 2026-04-03 Status: Production Ready Current Version: v2026.5.5 - Security Hardening